1. Field of the Invention
The invention relates to facial expression recognition systems and methods thereof, and more particularly to facial expression recognition systems and methods thereof capable of recognizing the facial expression of a plurality of facial images.
2. Description of the Related Art
With development of visual technology, many human-machine interaction mechanisms have been achieved by utilizing visual detection and recognition technology. For example, mobile robot interaction mechanisms (including automatic following), safe monitoring mechanisms and so on.
As to dynamic or static facial databases, the main work of an automated human facial recognition system consists of utilizing a facial image database to recognize one or more humans so as to serve as identifying or recognizing expression features. To achieve this objective, at first, the facial portion in the image has to be captured out of the image; and then a captured facial feature has to be performed to serve as a basis for comparison.
In the field of the human facial recognition, the most difficult technological challenge consists because a human face has numerous expressions/motion variations which may affect the accuracy of facial recognition. Thus, it is difficult to build a human facial recognition system having high recognizability and accuracy. In addition, for certain applications, it is also very important to recognize human emotions/facial expression using the facial image.
A conventional technology for recognizing facial expression by video sequence includes: detecting the human face and locating the human facial feature points by utilizing the automatic emotion feature point tracer; then, building feature for facial expression in accordance with the apparent motion vectors of facial feature points; and then classifying the features by a classifier. However, there are many variations in size, direction, light and background for the facial images retrieved from the video sequence, and poses, dressings, incomplete visibility of human faces, rotation angles and statuses of retrieved facial images may also affect the detection of the feature points. If the feature points of the retrieved facial images can not be successfully acquired, variations of the eyes and the mouth of the user in the continuous images can not be found, resulting in the failure of recognizing facial expression.
Further, because the facial expression of the users have detailed variations and may be different from person to person, which is difficult to be specially defined and descried, it is not easy to find the most discriminating expression information to determine facial expressions.